ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks
This addresses the need for reliable and generalizable explanations in GNNs, which is crucial for applications like drug discovery and social network analysis, though it appears incremental as it builds on existing GAN-based approaches.
The paper tackled the problem of explaining graph neural networks (GNNs) by proposing ACGAN-GNNExplainer, which uses an auxiliary classifier generative adversarial network to generate explanations, resulting in improved fidelity and accuracy compared to existing methods.
Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery. To address this challenge and enable reliable decision-making, many GNN explainers have been proposed in recent years. However, these methods often encounter limitations, including their dependence on specific instances, lack of generalizability to unseen graphs, producing potentially invalid explanations, and yielding inadequate fidelity. To overcome these limitations, we, in this paper, introduce the Auxiliary Classifier Generative Adversarial Network (ACGAN) into the field of GNN explanation and propose a new GNN explainer dubbed~\emph{ACGAN-GNNExplainer}. Our approach leverages a generator to produce explanations for the original input graphs while incorporating a discriminator to oversee the generation process, ensuring explanation fidelity and improving accuracy. Experimental evaluations conducted on both synthetic and real-world graph datasets demonstrate the superiority of our proposed method compared to other existing GNN explainers.